{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 实战练习之一 AI股票拟合算法"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输入必要的库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas            as pd\n",
    "import tensorflow        as tf   # type: ignore\n",
    "import numpy             as np\n",
    "import matplotlib.pyplot as plt # type: ignore\n",
    "# 支持中文\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "\n",
    "from numpy                 import array\n",
    "from sklearn               import metrics\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tensorflow.keras.models          import Sequential\n",
    "from tensorflow.keras.layers          import Dense,LSTM,Bidirectional\n",
    "\n",
    "from numpy.random   import seed\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['False_',\n",
       " 'ScalarType',\n",
       " 'True_',\n",
       " '_CopyMode',\n",
       " '_NoValue',\n",
       " '__NUMPY_SETUP__',\n",
       " '__all__',\n",
       " '__array_api_version__',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__config__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__expired_attributes__',\n",
       " '__file__',\n",
       " '__former_attrs__',\n",
       " '__future_scalars__',\n",
       " '__getattr__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__numpy_submodules__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " '__version__',\n",
       " '_core',\n",
       " '_distributor_init',\n",
       " '_expired_attrs_2_0',\n",
       " '_get_promotion_state',\n",
       " '_globals',\n",
       " '_int_extended_msg',\n",
       " '_mat',\n",
       " '_msg',\n",
       " '_no_nep50_warning',\n",
       " '_pyinstaller_hooks_dir',\n",
       " '_pytesttester',\n",
       " '_set_promotion_state',\n",
       " '_specific_msg',\n",
       " '_type_info',\n",
       " '_typing',\n",
       " '_utils',\n",
       " 'abs',\n",
       " 'absolute',\n",
       " 'acos',\n",
       " 'acosh',\n",
       " 'add',\n",
       " 'all',\n",
       " 'allclose',\n",
       " 'amax',\n",
       " 'amin',\n",
       " 'angle',\n",
       " 'any',\n",
       " 'append',\n",
       " 'apply_along_axis',\n",
       " 'apply_over_axes',\n",
       " 'arange',\n",
       " 'arccos',\n",
       " 'arccosh',\n",
       " 'arcsin',\n",
       " 'arcsinh',\n",
       " 'arctan',\n",
       " 'arctan2',\n",
       " 'arctanh',\n",
       " 'argmax',\n",
       " 'argmin',\n",
       " 'argpartition',\n",
       " 'argsort',\n",
       " 'argwhere',\n",
       " 'around',\n",
       " 'array',\n",
       " 'array2string',\n",
       " 'array_equal',\n",
       " 'array_equiv',\n",
       " 'array_repr',\n",
       " 'array_split',\n",
       " 'array_str',\n",
       " 'asanyarray',\n",
       " 'asarray',\n",
       " 'asarray_chkfinite',\n",
       " 'ascontiguousarray',\n",
       " 'asfortranarray',\n",
       " 'asin',\n",
       " 'asinh',\n",
       " 'asmatrix',\n",
       " 'astype',\n",
       " 'atan',\n",
       " 'atan2',\n",
       " 'atanh',\n",
       " 'atleast_1d',\n",
       " 'atleast_2d',\n",
       " 'atleast_3d',\n",
       " 'average',\n",
       " 'bartlett',\n",
       " 'base_repr',\n",
       " 'binary_repr',\n",
       " 'bincount',\n",
       " 'bitwise_and',\n",
       " 'bitwise_count',\n",
       " 'bitwise_invert',\n",
       " 'bitwise_left_shift',\n",
       " 'bitwise_not',\n",
       " 'bitwise_or',\n",
       " 'bitwise_right_shift',\n",
       " 'bitwise_xor',\n",
       " 'blackman',\n",
       " 'block',\n",
       " 'bmat',\n",
       " 'bool',\n",
       " 'bool_',\n",
       " 'broadcast',\n",
       " 'broadcast_arrays',\n",
       " 'broadcast_shapes',\n",
       " 'broadcast_to',\n",
       " 'busday_count',\n",
       " 'busday_offset',\n",
       " 'busdaycalendar',\n",
       " 'byte',\n",
       " 'bytes_',\n",
       " 'c_',\n",
       " 'can_cast',\n",
       " 'cbrt',\n",
       " 'cdouble',\n",
       " 'ceil',\n",
       " 'char',\n",
       " 'character',\n",
       " 'choose',\n",
       " 'clip',\n",
       " 'clongdouble',\n",
       " 'column_stack',\n",
       " 'common_type',\n",
       " 'complex128',\n",
       " 'complex64',\n",
       " 'complexfloating',\n",
       " 'compress',\n",
       " 'concat',\n",
       " 'concatenate',\n",
       " 'conj',\n",
       " 'conjugate',\n",
       " 'convolve',\n",
       " 'copy',\n",
       " 'copysign',\n",
       " 'copyto',\n",
       " 'core',\n",
       " 'corrcoef',\n",
       " 'correlate',\n",
       " 'cos',\n",
       " 'cosh',\n",
       " 'count_nonzero',\n",
       " 'cov',\n",
       " 'cross',\n",
       " 'csingle',\n",
       " 'ctypeslib',\n",
       " 'cumprod',\n",
       " 'cumsum',\n",
       " 'datetime64',\n",
       " 'datetime_as_string',\n",
       " 'datetime_data',\n",
       " 'deg2rad',\n",
       " 'degrees',\n",
       " 'delete',\n",
       " 'diag',\n",
       " 'diag_indices',\n",
       " 'diag_indices_from',\n",
       " 'diagflat',\n",
       " 'diagonal',\n",
       " 'diff',\n",
       " 'digitize',\n",
       " 'divide',\n",
       " 'divmod',\n",
       " 'dot',\n",
       " 'double',\n",
       " 'dsplit',\n",
       " 'dstack',\n",
       " 'dtype',\n",
       " 'dtypes',\n",
       " 'e',\n",
       " 'ediff1d',\n",
       " 'einsum',\n",
       " 'einsum_path',\n",
       " 'emath',\n",
       " 'empty',\n",
       " 'empty_like',\n",
       " 'equal',\n",
       " 'errstate',\n",
       " 'euler_gamma',\n",
       " 'exceptions',\n",
       " 'exp',\n",
       " 'exp2',\n",
       " 'expand_dims',\n",
       " 'expm1',\n",
       " 'extract',\n",
       " 'eye',\n",
       " 'f2py',\n",
       " 'fabs',\n",
       " 'fft',\n",
       " 'fill_diagonal',\n",
       " 'finfo',\n",
       " 'fix',\n",
       " 'flatiter',\n",
       " 'flatnonzero',\n",
       " 'flexible',\n",
       " 'flip',\n",
       " 'fliplr',\n",
       " 'flipud',\n",
       " 'float16',\n",
       " 'float32',\n",
       " 'float64',\n",
       " 'float_power',\n",
       " 'floating',\n",
       " 'floor',\n",
       " 'floor_divide',\n",
       " 'fmax',\n",
       " 'fmin',\n",
       " 'fmod',\n",
       " 'format_float_positional',\n",
       " 'format_float_scientific',\n",
       " 'frexp',\n",
       " 'from_dlpack',\n",
       " 'frombuffer',\n",
       " 'fromfile',\n",
       " 'fromfunction',\n",
       " 'fromiter',\n",
       " 'frompyfunc',\n",
       " 'fromregex',\n",
       " 'fromstring',\n",
       " 'full',\n",
       " 'full_like',\n",
       " 'gcd',\n",
       " 'generic',\n",
       " 'genfromtxt',\n",
       " 'geomspace',\n",
       " 'get_include',\n",
       " 'get_printoptions',\n",
       " 'getbufsize',\n",
       " 'geterr',\n",
       " 'geterrcall',\n",
       " 'gradient',\n",
       " 'greater',\n",
       " 'greater_equal',\n",
       " 'half',\n",
       " 'hamming',\n",
       " 'hanning',\n",
       " 'heaviside',\n",
       " 'histogram',\n",
       " 'histogram2d',\n",
       " 'histogram_bin_edges',\n",
       " 'histogramdd',\n",
       " 'hsplit',\n",
       " 'hstack',\n",
       " 'hypot',\n",
       " 'i0',\n",
       " 'identity',\n",
       " 'iinfo',\n",
       " 'imag',\n",
       " 'in1d',\n",
       " 'index_exp',\n",
       " 'indices',\n",
       " 'inexact',\n",
       " 'inf',\n",
       " 'info',\n",
       " 'inner',\n",
       " 'insert',\n",
       " 'int16',\n",
       " 'int32',\n",
       " 'int64',\n",
       " 'int8',\n",
       " 'int_',\n",
       " 'intc',\n",
       " 'integer',\n",
       " 'interp',\n",
       " 'intersect1d',\n",
       " 'intp',\n",
       " 'invert',\n",
       " 'is_busday',\n",
       " 'isclose',\n",
       " 'iscomplex',\n",
       " 'iscomplexobj',\n",
       " 'isdtype',\n",
       " 'isfinite',\n",
       " 'isfortran',\n",
       " 'isin',\n",
       " 'isinf',\n",
       " 'isnan',\n",
       " 'isnat',\n",
       " 'isneginf',\n",
       " 'isposinf',\n",
       " 'isreal',\n",
       " 'isrealobj',\n",
       " 'isscalar',\n",
       " 'issubdtype',\n",
       " 'iterable',\n",
       " 'ix_',\n",
       " 'kaiser',\n",
       " 'kron',\n",
       " 'lcm',\n",
       " 'ldexp',\n",
       " 'left_shift',\n",
       " 'less',\n",
       " 'less_equal',\n",
       " 'lexsort',\n",
       " 'lib',\n",
       " 'linalg',\n",
       " 'linspace',\n",
       " 'little_endian',\n",
       " 'load',\n",
       " 'loadtxt',\n",
       " 'log',\n",
       " 'log10',\n",
       " 'log1p',\n",
       " 'log2',\n",
       " 'logaddexp',\n",
       " 'logaddexp2',\n",
       " 'logical_and',\n",
       " 'logical_not',\n",
       " 'logical_or',\n",
       " 'logical_xor',\n",
       " 'logspace',\n",
       " 'long',\n",
       " 'longdouble',\n",
       " 'longlong',\n",
       " 'ma',\n",
       " 'mask_indices',\n",
       " 'matmul',\n",
       " 'matrix',\n",
       " 'matrix_transpose',\n",
       " 'max',\n",
       " 'maximum',\n",
       " 'may_share_memory',\n",
       " 'mean',\n",
       " 'median',\n",
       " 'memmap',\n",
       " 'meshgrid',\n",
       " 'mgrid',\n",
       " 'min',\n",
       " 'min_scalar_type',\n",
       " 'minimum',\n",
       " 'mintypecode',\n",
       " 'mod',\n",
       " 'modf',\n",
       " 'moveaxis',\n",
       " 'multiply',\n",
       " 'nan',\n",
       " 'nan_to_num',\n",
       " 'nanargmax',\n",
       " 'nanargmin',\n",
       " 'nancumprod',\n",
       " 'nancumsum',\n",
       " 'nanmax',\n",
       " 'nanmean',\n",
       " 'nanmedian',\n",
       " 'nanmin',\n",
       " 'nanpercentile',\n",
       " 'nanprod',\n",
       " 'nanquantile',\n",
       " 'nanstd',\n",
       " 'nansum',\n",
       " 'nanvar',\n",
       " 'ndarray',\n",
       " 'ndenumerate',\n",
       " 'ndim',\n",
       " 'ndindex',\n",
       " 'nditer',\n",
       " 'negative',\n",
       " 'nested_iters',\n",
       " 'newaxis',\n",
       " 'nextafter',\n",
       " 'nonzero',\n",
       " 'not_equal',\n",
       " 'number',\n",
       " 'object_',\n",
       " 'ogrid',\n",
       " 'ones',\n",
       " 'ones_like',\n",
       " 'outer',\n",
       " 'packbits',\n",
       " 'pad',\n",
       " 'partition',\n",
       " 'percentile',\n",
       " 'permute_dims',\n",
       " 'pi',\n",
       " 'piecewise',\n",
       " 'place',\n",
       " 'poly',\n",
       " 'poly1d',\n",
       " 'polyadd',\n",
       " 'polyder',\n",
       " 'polydiv',\n",
       " 'polyfit',\n",
       " 'polyint',\n",
       " 'polymul',\n",
       " 'polynomial',\n",
       " 'polysub',\n",
       " 'polyval',\n",
       " 'positive',\n",
       " 'pow',\n",
       " 'power',\n",
       " 'printoptions',\n",
       " 'prod',\n",
       " 'promote_types',\n",
       " 'ptp',\n",
       " 'put',\n",
       " 'put_along_axis',\n",
       " 'putmask',\n",
       " 'quantile',\n",
       " 'r_',\n",
       " 'rad2deg',\n",
       " 'radians',\n",
       " 'random',\n",
       " 'ravel',\n",
       " 'ravel_multi_index',\n",
       " 'real',\n",
       " 'real_if_close',\n",
       " 'rec',\n",
       " 'recarray',\n",
       " 'reciprocal',\n",
       " 'record',\n",
       " 'remainder',\n",
       " 'repeat',\n",
       " 'require',\n",
       " 'reshape',\n",
       " 'resize',\n",
       " 'result_type',\n",
       " 'right_shift',\n",
       " 'rint',\n",
       " 'roll',\n",
       " 'rollaxis',\n",
       " 'roots',\n",
       " 'rot90',\n",
       " 'round',\n",
       " 'row_stack',\n",
       " 's_',\n",
       " 'save',\n",
       " 'savetxt',\n",
       " 'savez',\n",
       " 'savez_compressed',\n",
       " 'sctypeDict',\n",
       " 'searchsorted',\n",
       " 'select',\n",
       " 'set_printoptions',\n",
       " 'setbufsize',\n",
       " 'setdiff1d',\n",
       " 'seterr',\n",
       " 'seterrcall',\n",
       " 'setxor1d',\n",
       " 'shape',\n",
       " 'shares_memory',\n",
       " 'short',\n",
       " 'show_config',\n",
       " 'show_runtime',\n",
       " 'sign',\n",
       " 'signbit',\n",
       " 'signedinteger',\n",
       " 'sin',\n",
       " 'sinc',\n",
       " 'single',\n",
       " 'sinh',\n",
       " 'size',\n",
       " 'sort',\n",
       " 'sort_complex',\n",
       " 'spacing',\n",
       " 'split',\n",
       " 'sqrt',\n",
       " 'square',\n",
       " 'squeeze',\n",
       " 'stack',\n",
       " 'std',\n",
       " 'str_',\n",
       " 'strings',\n",
       " 'subtract',\n",
       " 'sum',\n",
       " 'swapaxes',\n",
       " 'take',\n",
       " 'take_along_axis',\n",
       " 'tan',\n",
       " 'tanh',\n",
       " 'tensordot',\n",
       " 'test',\n",
       " 'testing',\n",
       " 'tile',\n",
       " 'timedelta64',\n",
       " 'trace',\n",
       " 'transpose',\n",
       " 'trapezoid',\n",
       " 'trapz',\n",
       " 'tri',\n",
       " 'tril',\n",
       " 'tril_indices',\n",
       " 'tril_indices_from',\n",
       " 'trim_zeros',\n",
       " 'triu',\n",
       " 'triu_indices',\n",
       " 'triu_indices_from',\n",
       " 'true_divide',\n",
       " 'trunc',\n",
       " 'typecodes',\n",
       " 'typename',\n",
       " 'typing',\n",
       " 'ubyte',\n",
       " 'ufunc',\n",
       " 'uint',\n",
       " 'uint16',\n",
       " 'uint32',\n",
       " 'uint64',\n",
       " 'uint8',\n",
       " 'uintc',\n",
       " 'uintp',\n",
       " 'ulong',\n",
       " 'ulonglong',\n",
       " 'union1d',\n",
       " 'unique',\n",
       " 'unique_all',\n",
       " 'unique_counts',\n",
       " 'unique_inverse',\n",
       " 'unique_values',\n",
       " 'unpackbits',\n",
       " 'unravel_index',\n",
       " 'unsignedinteger',\n",
       " 'unwrap',\n",
       " 'ushort',\n",
       " 'vander',\n",
       " 'var',\n",
       " 'vdot',\n",
       " 'vecdot',\n",
       " 'vectorize',\n",
       " 'void',\n",
       " 'vsplit',\n",
       " 'vstack',\n",
       " 'where',\n",
       " 'zeros',\n",
       " 'zeros_like']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.__all__\n",
    "np.__dict__\n",
    "np.__doc__\n",
    "np.__file__\n",
    "#np.__package__\n",
    "dir(np)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导入股票模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入tushare\n",
    "import tushare as ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MailMerge',\n",
       " 'TraderAPI',\n",
       " '__author__',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__doc__',\n",
       " '__file__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " '__version__',\n",
       " 'bar',\n",
       " 'bdi',\n",
       " 'broker_tops',\n",
       " 'cap_tops',\n",
       " 'close_apis',\n",
       " 'codecs',\n",
       " 'coins',\n",
       " 'coins_bar',\n",
       " 'coins_snapshot',\n",
       " 'coins_tick',\n",
       " 'coins_trade',\n",
       " 'day_boxoffice',\n",
       " 'day_cinema',\n",
       " 'forecast_data',\n",
       " 'fund',\n",
       " 'fund_holdings',\n",
       " 'futures',\n",
       " 'get_apis',\n",
       " 'get_area_classified',\n",
       " 'get_balance_sheet',\n",
       " 'get_cash_flow',\n",
       " 'get_cashflow_data',\n",
       " 'get_cffex_daily',\n",
       " 'get_concept_classified',\n",
       " 'get_cpi',\n",
       " 'get_czce_daily',\n",
       " 'get_day_all',\n",
       " 'get_dce_daily',\n",
       " 'get_debtpaying_data',\n",
       " 'get_deposit_rate',\n",
       " 'get_fund_info',\n",
       " 'get_future_daily',\n",
       " 'get_gdp_contrib',\n",
       " 'get_gdp_for',\n",
       " 'get_gdp_pull',\n",
       " 'get_gdp_quarter',\n",
       " 'get_gdp_year',\n",
       " 'get_gem_classified',\n",
       " 'get_gold_and_foreign_reserves',\n",
       " 'get_growth_data',\n",
       " 'get_h_data',\n",
       " 'get_hist_data',\n",
       " 'get_hists',\n",
       " 'get_hs300s',\n",
       " 'get_index',\n",
       " 'get_industry_classified',\n",
       " 'get_instrument',\n",
       " 'get_intlfuture',\n",
       " 'get_k_data',\n",
       " 'get_latest_news',\n",
       " 'get_loan_rate',\n",
       " 'get_markets',\n",
       " 'get_money_supply',\n",
       " 'get_money_supply_bal',\n",
       " 'get_nav_close',\n",
       " 'get_nav_grading',\n",
       " 'get_nav_history',\n",
       " 'get_nav_open',\n",
       " 'get_notices',\n",
       " 'get_operation_data',\n",
       " 'get_ppi',\n",
       " 'get_profit_data',\n",
       " 'get_profit_statement',\n",
       " 'get_realtime_quotes',\n",
       " 'get_report_data',\n",
       " 'get_rrr',\n",
       " 'get_shfe_daily',\n",
       " 'get_shfe_vwap',\n",
       " 'get_sina_dd',\n",
       " 'get_sme_classified',\n",
       " 'get_st_classified',\n",
       " 'get_stock_basics',\n",
       " 'get_suspended',\n",
       " 'get_sz50s',\n",
       " 'get_terminated',\n",
       " 'get_tick_data',\n",
       " 'get_today_all',\n",
       " 'get_today_ticks',\n",
       " 'get_token',\n",
       " 'get_zz500s',\n",
       " 'global_realtime',\n",
       " 'guba_sina',\n",
       " 'ht_subs',\n",
       " 'inst_detail',\n",
       " 'inst_tops',\n",
       " 'internet',\n",
       " 'is_holiday',\n",
       " 'latest_content',\n",
       " 'lpr_data',\n",
       " 'lpr_ma_data',\n",
       " 'margin_detail',\n",
       " 'margin_offset',\n",
       " 'margin_target',\n",
       " 'margin_zsl',\n",
       " 'moneyflow_hsgt',\n",
       " 'month_boxoffice',\n",
       " 'new_cbonds',\n",
       " 'new_stocks',\n",
       " 'notice_content',\n",
       " 'os',\n",
       " 'pledged_detail',\n",
       " 'pro',\n",
       " 'pro_api',\n",
       " 'pro_bar',\n",
       " 'pro_bar_vip',\n",
       " 'profit_data',\n",
       " 'profit_divis',\n",
       " 'quotes',\n",
       " 'realtime_boxoffice',\n",
       " 'realtime_list',\n",
       " 'realtime_quote',\n",
       " 'realtime_tick',\n",
       " 'reset_instrument',\n",
       " 'set_token',\n",
       " 'sh_margin_details',\n",
       " 'sh_margins',\n",
       " 'shibor_data',\n",
       " 'shibor_ma_data',\n",
       " 'shibor_quote_data',\n",
       " 'stock',\n",
       " 'stock_issuance',\n",
       " 'stock_pledged',\n",
       " 'subs',\n",
       " 'sz_margin_details',\n",
       " 'sz_margins',\n",
       " 'tick',\n",
       " 'top10_holders',\n",
       " 'top_list',\n",
       " 'trade_cal',\n",
       " 'trader',\n",
       " 'util',\n",
       " 'xsg_data']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(ts)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tushare 初始化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function pro_api in module tushare.pro.data_pro:\n",
      "\n",
      "pro_api(token='', timeout=30)\n",
      "    初始化pro API,第一次可以通过ts.set_token('your token')来记录自己的token凭证，临时token可以通过本参数传入\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#pro=ts.get_hist_data('603722')\n",
    "help(ts.pro_api)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A股日线行情\n",
    "\n",
    "接口：daily，可以通过数据工具调试和查看数据   \n",
    "数据说明：交易日每天15点～16点之间入库。本接口是未复权行情，停牌期间不提供数据   \n",
    "调取说明：120积分每分钟内最多调取500次，每次6000条数据，相当于单次提取23年历史   \n",
    "描述：获取股票行情数据，或通过通用行情接口获取数据，包含了前后复权数据   \n",
    "\n",
    "<p><strong>输入参数</strong></p>\n",
    "<table>\n",
    "<thead>\n",
    "<tr>\n",
    "<th>名称</th>\n",
    "<th>类型</th>\n",
    "<th>必选</th>\n",
    "<th>描述</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody><tr>\n",
    "<td>ts_code</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>股票代码（支持多个股票同时提取，逗号分隔）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>trade_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>交易日期（YYYYMMDD）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>start_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>开始日期(YYYYMMDD)</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>end_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>结束日期(YYYYMMDD)</td>\n",
    "</tr>\n",
    "</tbody>\n",
    "</table>\n",
    "\n",
    "\n",
    "<p><strong>注：日期都填YYYYMMDD格式，比如20181010</strong></p>\n",
    "<p><strong>输出参数</strong></p>\n",
    "<table>\n",
    "<thead>\n",
    "<tr>\n",
    "<th>名称</th>\n",
    "<th>类型</th>\n",
    "<th>描述</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody><tr>\n",
    "<td>ts_code</td>\n",
    "<td>str</td>\n",
    "<td>股票代码</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>trade_date</td>\n",
    "<td>str</td>\n",
    "<td>交易日期</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>open</td>\n",
    "<td>float</td>\n",
    "<td>开盘价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>high</td>\n",
    "<td>float</td>\n",
    "<td>最高价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>low</td>\n",
    "<td>float</td>\n",
    "<td>最低价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>close</td>\n",
    "<td>float</td>\n",
    "<td>收盘价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>pre_close</td>\n",
    "<td>float</td>\n",
    "<td>昨收价(前复权)</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>change</td>\n",
    "<td>float</td>\n",
    "<td>涨跌额</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>pct_chg</td>\n",
    "<td>float</td>\n",
    "<td>涨跌幅 （未复权，如果是复权请用 <a href=\"https://tushare.pro/document/2?doc_id=109\">通用行情接口</a> ）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>vol</td>\n",
    "<td>float</td>\n",
    "<td>成交量 （手）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>amount</td>\n",
    "<td>float</td>\n",
    "<td>成交额 （千元）</td>\n",
    "</tr>\n",
    "</tbody></table>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### 初始化pro接口\n",
    "### 在这个网址https://tushare.pro/注册，并在此处获得token，https://tushare.pro/user/token\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ts_code trade_date  open  high   low  close  pre_close  change  \\\n",
      "0     601939.SH   20241225  8.78  9.02  8.77   8.88       8.78    0.10   \n",
      "1     601939.SH   20241224  8.60  8.78  8.59   8.78       8.62    0.16   \n",
      "2     601939.SH   20241223  8.45  8.73  8.45   8.62       8.48    0.14   \n",
      "3     601939.SH   20241220  8.47  8.60  8.46   8.48       8.48    0.00   \n",
      "4     601939.SH   20241219  8.52  8.58  8.40   8.48       8.55   -0.07   \n",
      "...         ...        ...   ...   ...   ...    ...        ...     ...   \n",
      "3622  601939.SH   20100108  6.01  6.08  5.99   6.04       6.02    0.02   \n",
      "3623  601939.SH   20100107  6.11  6.15  6.01   6.02       6.11   -0.09   \n",
      "3624  601939.SH   20100106  6.20  6.23  6.10   6.11       6.21   -0.10   \n",
      "3625  601939.SH   20100105  6.15  6.26  6.08   6.21       6.12    0.09   \n",
      "3626  601939.SH   20100104  6.21  6.27  6.12   6.12       6.19   -0.07   \n",
      "\n",
      "      pct_chg         vol       amount  \n",
      "0      1.1390  1453468.04  1290884.287  \n",
      "1      1.8561  1294148.26  1128814.466  \n",
      "2      1.6509  1499687.80  1294586.538  \n",
      "3      0.0000   801627.61   682584.497  \n",
      "4     -0.8187   902493.13   765971.927  \n",
      "...       ...         ...          ...  \n",
      "3622   0.3300   564801.55   340188.391  \n",
      "3623  -1.4700  1212983.59   736255.989  \n",
      "3624  -1.6100  1060383.44   654623.387  \n",
      "3625   1.4700  1230221.90   761187.887  \n",
      "3626  -1.1300  1139845.17   705783.462  \n",
      "\n",
      "[3627 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "pro = ts.pro_api('3927feaa15607722bba17be0663cf1fc62d367e44b5958698c605009')\n",
    "# 拉取数据\n",
    "df = pro.daily(**{\n",
    "    \"ts_code\": \"601939.SH\",\n",
    "    \"trade_date\": \"\",\n",
    "    \"start_date\": 20100101,\n",
    "    \"end_date\": 20241225,\n",
    "    \"offset\": \"\",\n",
    "    \"limit\": \"\"\n",
    "}, fields=[\n",
    "    \"ts_code\",\n",
    "    \"trade_date\",\n",
    "    \"open\",\n",
    "    \"high\",\n",
    "    \"low\",\n",
    "    \"close\",\n",
    "    \"pre_close\",\n",
    "    \"change\",\n",
    "    \"pct_chg\",\n",
    "    \"vol\",\n",
    "    \"amount\"\n",
    "])\n",
    "print(df)\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ts_code trade_date  open  high   low  close  pre_close  change  \\\n",
      "0     601939.SH   20241225  8.78  9.02  8.77   8.88       8.78    0.10   \n",
      "1     601939.SH   20241224  8.60  8.78  8.59   8.78       8.62    0.16   \n",
      "2     601939.SH   20241223  8.45  8.73  8.45   8.62       8.48    0.14   \n",
      "3     601939.SH   20241220  8.47  8.60  8.46   8.48       8.48    0.00   \n",
      "4     601939.SH   20241219  8.52  8.58  8.40   8.48       8.55   -0.07   \n",
      "...         ...        ...   ...   ...   ...    ...        ...     ...   \n",
      "3622  601939.SH   20100108  6.01  6.08  5.99   6.04       6.02    0.02   \n",
      "3623  601939.SH   20100107  6.11  6.15  6.01   6.02       6.11   -0.09   \n",
      "3624  601939.SH   20100106  6.20  6.23  6.10   6.11       6.21   -0.10   \n",
      "3625  601939.SH   20100105  6.15  6.26  6.08   6.21       6.12    0.09   \n",
      "3626  601939.SH   20100104  6.21  6.27  6.12   6.12       6.19   -0.07   \n",
      "\n",
      "      pct_chg         vol       amount  \n",
      "0      1.1390  1453468.04  1290884.287  \n",
      "1      1.8561  1294148.26  1128814.466  \n",
      "2      1.6509  1499687.80  1294586.538  \n",
      "3      0.0000   801627.61   682584.497  \n",
      "4     -0.8187   902493.13   765971.927  \n",
      "...       ...         ...          ...  \n",
      "3622   0.3300   564801.55   340188.391  \n",
      "3623  -1.4700  1212983.59   736255.989  \n",
      "3624  -1.6100  1060383.44   654623.387  \n",
      "3625   1.4700  1230221.90   761187.887  \n",
      "3626  -1.1300  1139845.17   705783.462  \n",
      "\n",
      "[3627 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "### 存储数据\n",
    "print(df)\n",
    "df.to_csv(\"stock601939.csv\")\n",
    "stockname='建设银行'\n",
    "stockcode='601939'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>3617</th>\n",
       "      <th>3618</th>\n",
       "      <th>3619</th>\n",
       "      <th>3620</th>\n",
       "      <th>3621</th>\n",
       "      <th>3622</th>\n",
       "      <th>3623</th>\n",
       "      <th>3624</th>\n",
       "      <th>3625</th>\n",
       "      <th>3626</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ts_code</th>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>...</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <td>20241225</td>\n",
       "      <td>20241224</td>\n",
       "      <td>20241223</td>\n",
       "      <td>20241220</td>\n",
       "      <td>20241219</td>\n",
       "      <td>20241218</td>\n",
       "      <td>20241217</td>\n",
       "      <td>20241216</td>\n",
       "      <td>20241213</td>\n",
       "      <td>20241212</td>\n",
       "      <td>...</td>\n",
       "      <td>20100115</td>\n",
       "      <td>20100114</td>\n",
       "      <td>20100113</td>\n",
       "      <td>20100112</td>\n",
       "      <td>20100111</td>\n",
       "      <td>20100108</td>\n",
       "      <td>20100107</td>\n",
       "      <td>20100106</td>\n",
       "      <td>20100105</td>\n",
       "      <td>20100104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open</th>\n",
       "      <td>8.78</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.47</td>\n",
       "      <td>8.52</td>\n",
       "      <td>8.52</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.31</td>\n",
       "      <td>8.29</td>\n",
       "      <td>8.22</td>\n",
       "      <td>...</td>\n",
       "      <td>5.9</td>\n",
       "      <td>5.9</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>6.63</td>\n",
       "      <td>6.01</td>\n",
       "      <td>6.11</td>\n",
       "      <td>6.2</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high</th>\n",
       "      <td>9.02</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.73</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.58</td>\n",
       "      <td>8.65</td>\n",
       "      <td>8.57</td>\n",
       "      <td>8.55</td>\n",
       "      <td>8.39</td>\n",
       "      <td>8.34</td>\n",
       "      <td>...</td>\n",
       "      <td>5.91</td>\n",
       "      <td>5.94</td>\n",
       "      <td>6.07</td>\n",
       "      <td>6.18</td>\n",
       "      <td>6.63</td>\n",
       "      <td>6.08</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.23</td>\n",
       "      <td>6.26</td>\n",
       "      <td>6.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low</th>\n",
       "      <td>8.77</td>\n",
       "      <td>8.59</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.46</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.49</td>\n",
       "      <td>8.44</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8.26</td>\n",
       "      <td>8.18</td>\n",
       "      <td>...</td>\n",
       "      <td>5.8</td>\n",
       "      <td>5.85</td>\n",
       "      <td>5.85</td>\n",
       "      <td>5.99</td>\n",
       "      <td>6.08</td>\n",
       "      <td>5.99</td>\n",
       "      <td>6.01</td>\n",
       "      <td>6.1</td>\n",
       "      <td>6.08</td>\n",
       "      <td>6.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>close</th>\n",
       "      <td>8.88</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.62</td>\n",
       "      <td>8.48</td>\n",
       "      <td>8.48</td>\n",
       "      <td>8.55</td>\n",
       "      <td>8.49</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8.31</td>\n",
       "      <td>...</td>\n",
       "      <td>5.88</td>\n",
       "      <td>5.89</td>\n",
       "      <td>5.86</td>\n",
       "      <td>6.17</td>\n",
       "      <td>6.14</td>\n",
       "      <td>6.04</td>\n",
       "      <td>6.02</td>\n",
       "      <td>6.11</td>\n",
       "      <td>6.21</td>\n",
       "      <td>6.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pre_close</th>\n",
       "      <td>8.78</td>\n",
       "      <td>8.62</td>\n",
       "      <td>8.48</td>\n",
       "      <td>8.48</td>\n",
       "      <td>8.55</td>\n",
       "      <td>8.49</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8.31</td>\n",
       "      <td>8.21</td>\n",
       "      <td>...</td>\n",
       "      <td>5.89</td>\n",
       "      <td>5.86</td>\n",
       "      <td>6.17</td>\n",
       "      <td>6.14</td>\n",
       "      <td>6.04</td>\n",
       "      <td>6.02</td>\n",
       "      <td>6.11</td>\n",
       "      <td>6.21</td>\n",
       "      <td>6.12</td>\n",
       "      <td>6.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>change</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.15</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.1</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.09</td>\n",
       "      <td>-0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_chg</th>\n",
       "      <td>1.139</td>\n",
       "      <td>1.8561</td>\n",
       "      <td>1.6509</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.8187</td>\n",
       "      <td>0.7067</td>\n",
       "      <td>0.4734</td>\n",
       "      <td>1.8072</td>\n",
       "      <td>-0.1203</td>\n",
       "      <td>1.218</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>0.51</td>\n",
       "      <td>-5.02</td>\n",
       "      <td>0.49</td>\n",
       "      <td>1.66</td>\n",
       "      <td>0.33</td>\n",
       "      <td>-1.47</td>\n",
       "      <td>-1.61</td>\n",
       "      <td>1.47</td>\n",
       "      <td>-1.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vol</th>\n",
       "      <td>1453468.04</td>\n",
       "      <td>1294148.26</td>\n",
       "      <td>1499687.8</td>\n",
       "      <td>801627.61</td>\n",
       "      <td>902493.13</td>\n",
       "      <td>1002938.34</td>\n",
       "      <td>887937.59</td>\n",
       "      <td>1239263.79</td>\n",
       "      <td>1138958.08</td>\n",
       "      <td>1088774.79</td>\n",
       "      <td>...</td>\n",
       "      <td>983743.58</td>\n",
       "      <td>1113058.27</td>\n",
       "      <td>2004459.18</td>\n",
       "      <td>1611011.98</td>\n",
       "      <td>2351778.34</td>\n",
       "      <td>564801.55</td>\n",
       "      <td>1212983.59</td>\n",
       "      <td>1060383.44</td>\n",
       "      <td>1230221.9</td>\n",
       "      <td>1139845.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount</th>\n",
       "      <td>1290884.287</td>\n",
       "      <td>1128814.466</td>\n",
       "      <td>1294586.538</td>\n",
       "      <td>682584.497</td>\n",
       "      <td>765971.927</td>\n",
       "      <td>859273.151</td>\n",
       "      <td>755171.01</td>\n",
       "      <td>1045535.19</td>\n",
       "      <td>948661.065</td>\n",
       "      <td>900918.867</td>\n",
       "      <td>...</td>\n",
       "      <td>577423.374</td>\n",
       "      <td>655157.343</td>\n",
       "      <td>1193187.793</td>\n",
       "      <td>979282.765</td>\n",
       "      <td>1475780.887</td>\n",
       "      <td>340188.391</td>\n",
       "      <td>736255.989</td>\n",
       "      <td>654623.387</td>\n",
       "      <td>761187.887</td>\n",
       "      <td>705783.462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 3627 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0            1            2           3           4     \\\n",
       "ts_code       601939.SH    601939.SH    601939.SH   601939.SH   601939.SH   \n",
       "trade_date     20241225     20241224     20241223    20241220    20241219   \n",
       "open               8.78          8.6         8.45        8.47        8.52   \n",
       "high               9.02         8.78         8.73         8.6        8.58   \n",
       "low                8.77         8.59         8.45        8.46         8.4   \n",
       "close              8.88         8.78         8.62        8.48        8.48   \n",
       "pre_close          8.78         8.62         8.48        8.48        8.55   \n",
       "change              0.1         0.16         0.14         0.0       -0.07   \n",
       "pct_chg           1.139       1.8561       1.6509         0.0     -0.8187   \n",
       "vol          1453468.04   1294148.26    1499687.8   801627.61   902493.13   \n",
       "amount      1290884.287  1128814.466  1294586.538  682584.497  765971.927   \n",
       "\n",
       "                  5          6           7           8           9     ...  \\\n",
       "ts_code      601939.SH  601939.SH   601939.SH   601939.SH   601939.SH  ...   \n",
       "trade_date    20241218   20241217    20241216    20241213    20241212  ...   \n",
       "open              8.52       8.45        8.31        8.29        8.22  ...   \n",
       "high              8.65       8.57        8.55        8.39        8.34  ...   \n",
       "low               8.49       8.44         8.3        8.26        8.18  ...   \n",
       "close             8.55       8.49        8.45         8.3        8.31  ...   \n",
       "pre_close         8.49       8.45         8.3        8.31        8.21  ...   \n",
       "change            0.06       0.04        0.15       -0.01         0.1  ...   \n",
       "pct_chg         0.7067     0.4734      1.8072     -0.1203       1.218  ...   \n",
       "vol         1002938.34  887937.59  1239263.79  1138958.08  1088774.79  ...   \n",
       "amount      859273.151  755171.01  1045535.19  948661.065  900918.867  ...   \n",
       "\n",
       "                  3617        3618         3619        3620         3621  \\\n",
       "ts_code      601939.SH   601939.SH    601939.SH   601939.SH    601939.SH   \n",
       "trade_date    20100115    20100114     20100113    20100112     20100111   \n",
       "open               5.9         5.9          6.0         6.1         6.63   \n",
       "high              5.91        5.94         6.07        6.18         6.63   \n",
       "low                5.8        5.85         5.85        5.99         6.08   \n",
       "close             5.88        5.89         5.86        6.17         6.14   \n",
       "pre_close         5.89        5.86         6.17        6.14         6.04   \n",
       "change           -0.01        0.03        -0.31        0.03          0.1   \n",
       "pct_chg          -0.17        0.51        -5.02        0.49         1.66   \n",
       "vol          983743.58  1113058.27   2004459.18  1611011.98   2351778.34   \n",
       "amount      577423.374  655157.343  1193187.793  979282.765  1475780.887   \n",
       "\n",
       "                  3622        3623        3624        3625        3626  \n",
       "ts_code      601939.SH   601939.SH   601939.SH   601939.SH   601939.SH  \n",
       "trade_date    20100108    20100107    20100106    20100105    20100104  \n",
       "open              6.01        6.11         6.2        6.15        6.21  \n",
       "high              6.08        6.15        6.23        6.26        6.27  \n",
       "low               5.99        6.01         6.1        6.08        6.12  \n",
       "close             6.04        6.02        6.11        6.21        6.12  \n",
       "pre_close         6.02        6.11        6.21        6.12        6.19  \n",
       "change            0.02       -0.09        -0.1        0.09       -0.07  \n",
       "pct_chg           0.33       -1.47       -1.61        1.47       -1.13  \n",
       "vol          564801.55  1212983.59  1060383.44   1230221.9  1139845.17  \n",
       "amount      340188.391  736255.989  654623.387  761187.887  705783.462  \n",
       "\n",
       "[11 rows x 3627 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据转置\n",
    "df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#输出图形\n",
    "df[\"close\"].plot(figsize=(16,4),legend=True)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c04f8bfd40>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#读取数据\n",
    "#coding=utf-8\n",
    "#使用pandas读取数据\n",
    "mydata=pd.read_csv(\"./stock601939.csv\",parse_dates=[\"trade_date\"],index_col=\"trade_date\")[[\"open\",\"high\",\"low\",\"close\"]]\n",
    "\n",
    "# data=pd.read_csv(\"./stock688333.csv\",parse_dates=[\"trade_date\"])[[\"trade_date\",\"open\",\"high\",\"low\",\"close\",\"vol\"]]\n",
    "\n",
    "#翻转数据\n",
    "\n",
    "mydata=mydata.iloc[::-1]\n",
    "\n",
    "plt.plot(mydata[\"close\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3597 entries, 0 to 3596\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   trade_date  3597 non-null   datetime64[ns]\n",
      " 1   open        3597 non-null   float64       \n",
      " 2   high        3597 non-null   float64       \n",
      " 3   low         3597 non-null   float64       \n",
      " 4   close       3597 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(4)\n",
      "memory usage: 140.6 KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3592</th>\n",
       "      <td>2024-12-19</td>\n",
       "      <td>8.52</td>\n",
       "      <td>8.58</td>\n",
       "      <td>8.40</td>\n",
       "      <td>8.48</td>\n",
       "      <td>8.454</td>\n",
       "      <td>8.368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3593</th>\n",
       "      <td>2024-12-20</td>\n",
       "      <td>8.47</td>\n",
       "      <td>8.60</td>\n",
       "      <td>8.46</td>\n",
       "      <td>8.48</td>\n",
       "      <td>8.490</td>\n",
       "      <td>8.389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3594</th>\n",
       "      <td>2024-12-23</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.73</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.62</td>\n",
       "      <td>8.524</td>\n",
       "      <td>8.426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3595</th>\n",
       "      <td>2024-12-24</td>\n",
       "      <td>8.60</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.59</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.582</td>\n",
       "      <td>8.467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3596</th>\n",
       "      <td>2024-12-25</td>\n",
       "      <td>8.78</td>\n",
       "      <td>9.02</td>\n",
       "      <td>8.77</td>\n",
       "      <td>8.88</td>\n",
       "      <td>8.648</td>\n",
       "      <td>8.534</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     trade_date  open  high   low  close      5     10\n",
       "3592 2024-12-19  8.52  8.58  8.40   8.48  8.454  8.368\n",
       "3593 2024-12-20  8.47  8.60  8.46   8.48  8.490  8.389\n",
       "3594 2024-12-23  8.45  8.73  8.45   8.62  8.524  8.426\n",
       "3595 2024-12-24  8.60  8.78  8.59   8.78  8.582  8.467\n",
       "3596 2024-12-25  8.78  9.02  8.77   8.88  8.648  8.534"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取股价数据\n",
    "# import akshare as ak\n",
    "\n",
    "df3 = mydata.reset_index().iloc[30:, :6]  # 取过去30天数据\n",
    "df3 = df3.dropna(how='any').reset_index(drop=True) #去除空值且从零开始编号索引\n",
    "df3 = df3.sort_values(by='trade_date', ascending=True)\n",
    "print(df3.info())\n",
    "\n",
    "# 计算均线数据\n",
    "df3['5'] = df3.close.rolling(5).mean()\n",
    "df3['10'] = df3.close.rolling(10).mean()\n",
    "\n",
    "df3.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "matplotlib.style.use('ggplot') #用于调整图标样式，可选\n",
    "\n",
    "import mplfinance as mpf\n",
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "from matplotlib.ticker import FormatStrFormatter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=200)\n",
    "\n",
    "candlestick2_ohlc(ax,\n",
    "                opens = df3[ 'open'].values,\n",
    "                highs = df3['high'].values,\n",
    "                lows = df3[ 'low'].values,\n",
    "                closes = df3['close'].values,\n",
    "                width=0.5, colorup=\"r\",colordown=\"g\")\n",
    "\n",
    "# 显示最高点和最低点\n",
    "ax.text( df3.high.idxmax(), df3.high.max(),   s =df3.high.max(), fontsize=8)\n",
    "ax.text( df3.high.idxmin(), df3.high.min()-2, s = df3.high.min(), fontsize=8)\n",
    "\n",
    "ax.set_facecolor(\"white\")\n",
    "ax.set_title(stockname, fontsize=8)\n",
    "\n",
    "# 画均线\n",
    "plt.plot(df3['5'].values, alpha = 0.5, label='MA5')\n",
    "plt.plot(df3['10'].values, alpha = 0.5, label='MA10')\n",
    "\n",
    "ax.legend(facecolor='white', edgecolor='white', fontsize=6)\n",
    "\n",
    "# 修改x轴坐标\n",
    "plt.xticks(ticks =  np.arange(0,len(df3)), labels = df3.trade_date.dt.strftime('%Y-%m-%d').to_numpy() )\n",
    "plt.xticks(rotation=45, size=8)\n",
    "\n",
    "# 修改y轴坐标\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>ts_code</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>change</th>\n",
       "      <th>pct_chg</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>20241225</td>\n",
       "      <td>8.78</td>\n",
       "      <td>9.02</td>\n",
       "      <td>8.77</td>\n",
       "      <td>8.88</td>\n",
       "      <td>8.78</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1.1390</td>\n",
       "      <td>1453468.04</td>\n",
       "      <td>1290884.287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>20241224</td>\n",
       "      <td>8.60</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.59</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.62</td>\n",
       "      <td>0.16</td>\n",
       "      <td>1.8561</td>\n",
       "      <td>1294148.26</td>\n",
       "      <td>1128814.466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>20241223</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.73</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.62</td>\n",
       "      <td>8.48</td>\n",
       "      <td>0.14</td>\n",
       "      <td>1.6509</td>\n",
       "      <td>1499687.80</td>\n",
       "      <td>1294586.538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>20241220</td>\n",
       "      <td>8.47</td>\n",
       "      <td>8.60</td>\n",
       "      <td>8.46</td>\n",
       "      <td>8.48</td>\n",
       "      <td>8.48</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>801627.61</td>\n",
       "      <td>682584.497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>20241219</td>\n",
       "      <td>8.52</td>\n",
       "      <td>8.58</td>\n",
       "      <td>8.40</td>\n",
       "      <td>8.48</td>\n",
       "      <td>8.55</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.8187</td>\n",
       "      <td>902493.13</td>\n",
       "      <td>765971.927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3622</th>\n",
       "      <td>3622</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>20100108</td>\n",
       "      <td>6.01</td>\n",
       "      <td>6.08</td>\n",
       "      <td>5.99</td>\n",
       "      <td>6.04</td>\n",
       "      <td>6.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.3300</td>\n",
       "      <td>564801.55</td>\n",
       "      <td>340188.391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3623</th>\n",
       "      <td>3623</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>20100107</td>\n",
       "      <td>6.11</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.01</td>\n",
       "      <td>6.02</td>\n",
       "      <td>6.11</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-1.4700</td>\n",
       "      <td>1212983.59</td>\n",
       "      <td>736255.989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3624</th>\n",
       "      <td>3624</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>20100106</td>\n",
       "      <td>6.20</td>\n",
       "      <td>6.23</td>\n",
       "      <td>6.10</td>\n",
       "      <td>6.11</td>\n",
       "      <td>6.21</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-1.6100</td>\n",
       "      <td>1060383.44</td>\n",
       "      <td>654623.387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3625</th>\n",
       "      <td>3625</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>20100105</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.26</td>\n",
       "      <td>6.08</td>\n",
       "      <td>6.21</td>\n",
       "      <td>6.12</td>\n",
       "      <td>0.09</td>\n",
       "      <td>1.4700</td>\n",
       "      <td>1230221.90</td>\n",
       "      <td>761187.887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3626</th>\n",
       "      <td>3626</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>20100104</td>\n",
       "      <td>6.21</td>\n",
       "      <td>6.27</td>\n",
       "      <td>6.12</td>\n",
       "      <td>6.12</td>\n",
       "      <td>6.19</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-1.1300</td>\n",
       "      <td>1139845.17</td>\n",
       "      <td>705783.462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3627 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0    ts_code  trade_date  open  high   low  close  pre_close  \\\n",
       "0              0  601939.SH    20241225  8.78  9.02  8.77   8.88       8.78   \n",
       "1              1  601939.SH    20241224  8.60  8.78  8.59   8.78       8.62   \n",
       "2              2  601939.SH    20241223  8.45  8.73  8.45   8.62       8.48   \n",
       "3              3  601939.SH    20241220  8.47  8.60  8.46   8.48       8.48   \n",
       "4              4  601939.SH    20241219  8.52  8.58  8.40   8.48       8.55   \n",
       "...          ...        ...         ...   ...   ...   ...    ...        ...   \n",
       "3622        3622  601939.SH    20100108  6.01  6.08  5.99   6.04       6.02   \n",
       "3623        3623  601939.SH    20100107  6.11  6.15  6.01   6.02       6.11   \n",
       "3624        3624  601939.SH    20100106  6.20  6.23  6.10   6.11       6.21   \n",
       "3625        3625  601939.SH    20100105  6.15  6.26  6.08   6.21       6.12   \n",
       "3626        3626  601939.SH    20100104  6.21  6.27  6.12   6.12       6.19   \n",
       "\n",
       "      change  pct_chg         vol       amount  \n",
       "0       0.10   1.1390  1453468.04  1290884.287  \n",
       "1       0.16   1.8561  1294148.26  1128814.466  \n",
       "2       0.14   1.6509  1499687.80  1294586.538  \n",
       "3       0.00   0.0000   801627.61   682584.497  \n",
       "4      -0.07  -0.8187   902493.13   765971.927  \n",
       "...      ...      ...         ...          ...  \n",
       "3622    0.02   0.3300   564801.55   340188.391  \n",
       "3623   -0.09  -1.4700  1212983.59   736255.989  \n",
       "3624   -0.10  -1.6100  1060383.44   654623.387  \n",
       "3625    0.09   1.4700  1230221.90   761187.887  \n",
       "3626   -0.07  -1.1300  1139845.17   705783.462  \n",
       "\n",
       "[3627 rows x 12 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "# 假设从CSV文件读取数据\n",
    "data = pd.read_csv('stock601939.csv')\n",
    "# 后续代码\n",
    "#from matplotlib.dates import date2num\n",
    "#data['date']=date2num(data.index.to_pydatetime())\n",
    "#data=data.sort_values(by=\"date\",ascending=True)\n",
    "data # type: ignore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>ts_code</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>change</th>\n",
       "      <th>pct_chg</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>20241225</td>\n",
       "      <td>8.78</td>\n",
       "      <td>9.02</td>\n",
       "      <td>8.77</td>\n",
       "      <td>8.88</td>\n",
       "      <td>8.78</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1.1390</td>\n",
       "      <td>1453468.04</td>\n",
       "      <td>1290884.287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>20241224</td>\n",
       "      <td>8.60</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.59</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.62</td>\n",
       "      <td>0.16</td>\n",
       "      <td>1.8561</td>\n",
       "      <td>1294148.26</td>\n",
       "      <td>1128814.466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>20241223</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.73</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.62</td>\n",
       "      <td>8.48</td>\n",
       "      <td>0.14</td>\n",
       "      <td>1.6509</td>\n",
       "      <td>1499687.80</td>\n",
       "      <td>1294586.538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>20241220</td>\n",
       "      <td>8.47</td>\n",
       "      <td>8.60</td>\n",
       "      <td>8.46</td>\n",
       "      <td>8.48</td>\n",
       "      <td>8.48</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>801627.61</td>\n",
       "      <td>682584.497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>20241219</td>\n",
       "      <td>8.52</td>\n",
       "      <td>8.58</td>\n",
       "      <td>8.40</td>\n",
       "      <td>8.48</td>\n",
       "      <td>8.55</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.8187</td>\n",
       "      <td>902493.13</td>\n",
       "      <td>765971.927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3622</th>\n",
       "      <td>3622</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>20100108</td>\n",
       "      <td>6.01</td>\n",
       "      <td>6.08</td>\n",
       "      <td>5.99</td>\n",
       "      <td>6.04</td>\n",
       "      <td>6.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.3300</td>\n",
       "      <td>564801.55</td>\n",
       "      <td>340188.391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3623</th>\n",
       "      <td>3623</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>20100107</td>\n",
       "      <td>6.11</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.01</td>\n",
       "      <td>6.02</td>\n",
       "      <td>6.11</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-1.4700</td>\n",
       "      <td>1212983.59</td>\n",
       "      <td>736255.989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3624</th>\n",
       "      <td>3624</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>20100106</td>\n",
       "      <td>6.20</td>\n",
       "      <td>6.23</td>\n",
       "      <td>6.10</td>\n",
       "      <td>6.11</td>\n",
       "      <td>6.21</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-1.6100</td>\n",
       "      <td>1060383.44</td>\n",
       "      <td>654623.387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3625</th>\n",
       "      <td>3625</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>20100105</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.26</td>\n",
       "      <td>6.08</td>\n",
       "      <td>6.21</td>\n",
       "      <td>6.12</td>\n",
       "      <td>0.09</td>\n",
       "      <td>1.4700</td>\n",
       "      <td>1230221.90</td>\n",
       "      <td>761187.887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3626</th>\n",
       "      <td>3626</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>20100104</td>\n",
       "      <td>6.21</td>\n",
       "      <td>6.27</td>\n",
       "      <td>6.12</td>\n",
       "      <td>6.12</td>\n",
       "      <td>6.19</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-1.1300</td>\n",
       "      <td>1139845.17</td>\n",
       "      <td>705783.462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3627 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0    ts_code  trade_date  open  high   low  close  pre_close  \\\n",
       "0              0  601939.SH    20241225  8.78  9.02  8.77   8.88       8.78   \n",
       "1              1  601939.SH    20241224  8.60  8.78  8.59   8.78       8.62   \n",
       "2              2  601939.SH    20241223  8.45  8.73  8.45   8.62       8.48   \n",
       "3              3  601939.SH    20241220  8.47  8.60  8.46   8.48       8.48   \n",
       "4              4  601939.SH    20241219  8.52  8.58  8.40   8.48       8.55   \n",
       "...          ...        ...         ...   ...   ...   ...    ...        ...   \n",
       "3622        3622  601939.SH    20100108  6.01  6.08  5.99   6.04       6.02   \n",
       "3623        3623  601939.SH    20100107  6.11  6.15  6.01   6.02       6.11   \n",
       "3624        3624  601939.SH    20100106  6.20  6.23  6.10   6.11       6.21   \n",
       "3625        3625  601939.SH    20100105  6.15  6.26  6.08   6.21       6.12   \n",
       "3626        3626  601939.SH    20100104  6.21  6.27  6.12   6.12       6.19   \n",
       "\n",
       "      change  pct_chg         vol       amount  \n",
       "0       0.10   1.1390  1453468.04  1290884.287  \n",
       "1       0.16   1.8561  1294148.26  1128814.466  \n",
       "2       0.14   1.6509  1499687.80  1294586.538  \n",
       "3       0.00   0.0000   801627.61   682584.497  \n",
       "4      -0.07  -0.8187   902493.13   765971.927  \n",
       "...      ...      ...         ...          ...  \n",
       "3622    0.02   0.3300   564801.55   340188.391  \n",
       "3623   -0.09  -1.4700  1212983.59   736255.989  \n",
       "3624   -0.10  -1.6100  1060383.44   654623.387  \n",
       "3625    0.09   1.4700  1230221.90   761187.887  \n",
       "3626   -0.07  -1.1300  1139845.17   705783.462  \n",
       "\n",
       "[3627 rows x 12 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#from matplotlib.dates  import date2num\n",
    "#data['date']=date2num(data.index.to_pydatetime())\n",
    "#data=data.sort_values(by=\"date\",ascending=True)\n",
    "data # type: ignore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     trade_date  open  high   low  close\n",
      "0    2010-01-04  6.21  6.27  6.12   6.12\n",
      "1    2010-01-05  6.15  6.26  6.08   6.21\n",
      "2    2010-01-06  6.20  6.23  6.10   6.11\n",
      "3    2010-01-07  6.11  6.15  6.01   6.02\n",
      "4    2010-01-08  6.01  6.08  5.99   6.04\n",
      "...         ...   ...   ...   ...    ...\n",
      "3622 2024-12-19  8.52  8.58  8.40   8.48\n",
      "3623 2024-12-20  8.47  8.60  8.46   8.48\n",
      "3624 2024-12-23  8.45  8.73  8.45   8.62\n",
      "3625 2024-12-24  8.60  8.78  8.59   8.78\n",
      "3626 2024-12-25  8.78  9.02  8.77   8.88\n",
      "\n",
      "[3627 rows x 5 columns]\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3627 entries, 0 to 3626\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   trade_date  3627 non-null   datetime64[ns]\n",
      " 1   open        3627 non-null   float64       \n",
      " 2   high        3627 non-null   float64       \n",
      " 3   low         3627 non-null   float64       \n",
      " 4   close       3627 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(4)\n",
      "memory usage: 141.8 KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "      <th>30</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>6.21</td>\n",
       "      <td>6.27</td>\n",
       "      <td>6.12</td>\n",
       "      <td>6.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.26</td>\n",
       "      <td>6.08</td>\n",
       "      <td>6.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-06</td>\n",
       "      <td>6.20</td>\n",
       "      <td>6.23</td>\n",
       "      <td>6.10</td>\n",
       "      <td>6.11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-07</td>\n",
       "      <td>6.11</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.01</td>\n",
       "      <td>6.02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-08</td>\n",
       "      <td>6.01</td>\n",
       "      <td>6.08</td>\n",
       "      <td>5.99</td>\n",
       "      <td>6.04</td>\n",
       "      <td>6.100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3622</th>\n",
       "      <td>2024-12-19</td>\n",
       "      <td>8.52</td>\n",
       "      <td>8.58</td>\n",
       "      <td>8.40</td>\n",
       "      <td>8.48</td>\n",
       "      <td>8.454</td>\n",
       "      <td>8.368</td>\n",
       "      <td>8.106333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3623</th>\n",
       "      <td>2024-12-20</td>\n",
       "      <td>8.47</td>\n",
       "      <td>8.60</td>\n",
       "      <td>8.46</td>\n",
       "      <td>8.48</td>\n",
       "      <td>8.490</td>\n",
       "      <td>8.389</td>\n",
       "      <td>8.122333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3624</th>\n",
       "      <td>2024-12-23</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.73</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.62</td>\n",
       "      <td>8.524</td>\n",
       "      <td>8.426</td>\n",
       "      <td>8.146333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3625</th>\n",
       "      <td>2024-12-24</td>\n",
       "      <td>8.60</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.59</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.582</td>\n",
       "      <td>8.467</td>\n",
       "      <td>8.177333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3626</th>\n",
       "      <td>2024-12-25</td>\n",
       "      <td>8.78</td>\n",
       "      <td>9.02</td>\n",
       "      <td>8.77</td>\n",
       "      <td>8.88</td>\n",
       "      <td>8.648</td>\n",
       "      <td>8.534</td>\n",
       "      <td>8.211000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3627 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     trade_date  open  high   low  close      5     10        30\n",
       "0    2010-01-04  6.21  6.27  6.12   6.12    NaN    NaN       NaN\n",
       "1    2010-01-05  6.15  6.26  6.08   6.21    NaN    NaN       NaN\n",
       "2    2010-01-06  6.20  6.23  6.10   6.11    NaN    NaN       NaN\n",
       "3    2010-01-07  6.11  6.15  6.01   6.02    NaN    NaN       NaN\n",
       "4    2010-01-08  6.01  6.08  5.99   6.04  6.100    NaN       NaN\n",
       "...         ...   ...   ...   ...    ...    ...    ...       ...\n",
       "3622 2024-12-19  8.52  8.58  8.40   8.48  8.454  8.368  8.106333\n",
       "3623 2024-12-20  8.47  8.60  8.46   8.48  8.490  8.389  8.122333\n",
       "3624 2024-12-23  8.45  8.73  8.45   8.62  8.524  8.426  8.146333\n",
       "3625 2024-12-24  8.60  8.78  8.59   8.78  8.582  8.467  8.177333\n",
       "3626 2024-12-25  8.78  9.02  8.77   8.88  8.648  8.534  8.211000\n",
       "\n",
       "[3627 rows x 8 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取股价数据\n",
    "df3 = mydata.reset_index().iloc[:,:]  #取过去30天数据\n",
    "df3 = df3.dropna(how='any').reset_index(drop=True) #去除空值且从零开始编号索引\n",
    "print(df3)\n",
    "df3 = df3.sort_values(by='trade_date', ascending=True)\n",
    "print(df3.info())\n",
    "\n",
    "# 均线数据\n",
    "df3['5'] = df3.close.rolling(5).mean()\n",
    "df3['10'] = df3.close.rolling(10).mean()\n",
    "df3['30']=df3.close.rolling(30).mean()\n",
    "\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.style.use('ggplot') #用于调整图标样式，可选\n",
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=200)\n",
    "\n",
    "candlestick2_ohlc(ax,\n",
    "                opens = df3[ 'open'].values,\n",
    "                highs = df3['high'].values,\n",
    "                lows = df3[ 'low'].values,\n",
    "                closes = df3['close'].values,\n",
    "                width=0.5, colorup=\"r\",colordown=\"g\")\n",
    "\n",
    "# 显示最高点和最低点\n",
    "ax.text( df3.high.idxmax(), df3.high.max(),   s =df3.high.max(), fontsize=8)\n",
    "ax.text( df3.high.idxmin(), df3.high.min()-2, s = df3.high.min(), fontsize=8)\n",
    "\n",
    "ax.set_facecolor(\"white\")\n",
    "ax.set_title(stockname+\"股价走势\", fontsize=8)\n",
    "\n",
    "# 画均线\n",
    "plt.plot(df3['5'].values, alpha = 0.5, label='MA5')\n",
    "plt.plot(df3['10'].values, alpha = 0.5, label='MA10')\n",
    "plt.plot(df3['30'].values,alpha=0.5, label='MA30')\n",
    "\n",
    "ax.legend(facecolor='white', edgecolor='white', fontsize=6)\n",
    "\n",
    "# 修改x轴坐标\n",
    "tempXticks=np.arange(0,len(df3))\n",
    "#XticksData=data.asfreq(\"2M\").dropna()\n",
    "nameXticks =  df3.trade_date.dt.strftime('%Y-%m-%d').to_numpy()\n",
    "\n",
    "plt.xticks(ticks =tempXticks , labels =nameXticks )\n",
    "plt.xticks(rotation=45, size=8)\n",
    "#修改X轴间隔\n",
    "x_major_locator=plt.MultipleLocator(10)\n",
    "ax.xaxis.set_major_locator(x_major_locator)\n",
    "ax.spines['bottom'].set_color('red')\n",
    "ax.spines['left'].set_color('red')\n",
    "plt.xlim(0,100)\n",
    "# 修改y轴坐标\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data1=pd.DataFrame(data,columns=[\"trade_date\",\"close\"])\n",
    "#data1=pd.DataFrame(data,columns=[\"close\"])\n",
    "data1=mydata\n",
    "data1[\"open\"].plot(label=\"open\")\n",
    "data1[\"close\"].plot(label=\"close\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data1' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[1], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m ddd\u001b[38;5;241m=\u001b[39m[]\n\u001b[1;32m----> 2\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m item \u001b[38;5;129;01min\u001b[39;00m \u001b[43mdata1\u001b[49m\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m      3\u001b[0m     ddd\u001b[38;5;241m.\u001b[39mappend(item)\n\u001b[0;32m      4\u001b[0m ind\u001b[38;5;241m=\u001b[39mddd[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39mvalues\n",
      "\u001b[1;31mNameError\u001b[0m: name 'data1' is not defined"
     ]
    }
   ],
   "source": [
    "ddd=[]\n",
    "for item in data1.items():\n",
    "    ddd.append(item)\n",
    "ind=ddd[0][1].values\n",
    "da1=ddd[1][1].values\n",
    "index1=[]\n",
    "data2=[]\n",
    "for item in ind:\n",
    "    index1.append(item)\n",
    "index1.reverse()\n",
    "for item in da1:\n",
    "    data2.append(item)\n",
    "data2.reverse()\n",
    "data1={'trade_date':index1,'close':data2}\n",
    "stockdata=pd.DataFrame(data1)\n",
    "\n",
    "stockdata.items    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6.12],\n",
       "       [6.21],\n",
       "       [6.11],\n",
       "       ...,\n",
       "       [8.62],\n",
       "       [8.78],\n",
       "       [8.88]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据检查\n",
    "df3.iloc[:,4:5].values[:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.dates  import date2num\n",
    "date2num(df3.trade_date.values)\n",
    "#添加data数据列\n",
    "df3['date']=date2num(df3.trade_date.values)\n",
    "#df3=df3.sort_values(by=\"date\",ascending=True)\n",
    "#df3=df3[['date','open', 'high', 'low', 'close']]\n",
    "\n",
    "ind=np.arange(0,2677)\n",
    "dataf1=df3[['date','open', 'high', 'low', 'close']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14613.0</th>\n",
       "      <td>6.21</td>\n",
       "      <td>6.27</td>\n",
       "      <td>6.12</td>\n",
       "      <td>6.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14614.0</th>\n",
       "      <td>6.15</td>\n",
       "      <td>6.26</td>\n",
       "      <td>6.08</td>\n",
       "      <td>6.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14615.0</th>\n",
       "      <td>6.20</td>\n",
       "      <td>6.23</td>\n",
       "      <td>6.10</td>\n",
       "      <td>6.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14616.0</th>\n",
       "      <td>6.11</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.01</td>\n",
       "      <td>6.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14617.0</th>\n",
       "      <td>6.01</td>\n",
       "      <td>6.08</td>\n",
       "      <td>5.99</td>\n",
       "      <td>6.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20076.0</th>\n",
       "      <td>8.52</td>\n",
       "      <td>8.58</td>\n",
       "      <td>8.40</td>\n",
       "      <td>8.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20077.0</th>\n",
       "      <td>8.47</td>\n",
       "      <td>8.60</td>\n",
       "      <td>8.46</td>\n",
       "      <td>8.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20080.0</th>\n",
       "      <td>8.45</td>\n",
       "      <td>8.73</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20081.0</th>\n",
       "      <td>8.60</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.59</td>\n",
       "      <td>8.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20082.0</th>\n",
       "      <td>8.78</td>\n",
       "      <td>9.02</td>\n",
       "      <td>8.77</td>\n",
       "      <td>8.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3627 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         open  high   low  close\n",
       "date                            \n",
       "14613.0  6.21  6.27  6.12   6.12\n",
       "14614.0  6.15  6.26  6.08   6.21\n",
       "14615.0  6.20  6.23  6.10   6.11\n",
       "14616.0  6.11  6.15  6.01   6.02\n",
       "14617.0  6.01  6.08  5.99   6.04\n",
       "...       ...   ...   ...    ...\n",
       "20076.0  8.52  8.58  8.40   8.48\n",
       "20077.0  8.47  8.60  8.46   8.48\n",
       "20080.0  8.45  8.73  8.45   8.62\n",
       "20081.0  8.60  8.78  8.59   8.78\n",
       "20082.0  8.78  9.02  8.77   8.88\n",
       "\n",
       "[3627 rows x 4 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataf1.set_index(\"date\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 深度学习拟合股票"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 确保结果尽可能重现\n",
    "\n",
    "seed(1)\n",
    "tf.random.set_seed(1)\n",
    "\n",
    "# 设置相关参数\n",
    "n_timestamp  = 5    # 时间戳\n",
    "n_epochs     = 30    # 训练轮数\n",
    "# ====================================\n",
    "#      选择模型：\n",
    "#            1: 单层 LSTM\n",
    "#            2: 多层 LSTM\n",
    "#            3: 双向 LSTM\n",
    "# ====================================\n",
    "model_type = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14613.0</td>\n",
       "      <td>6.21</td>\n",
       "      <td>6.27</td>\n",
       "      <td>6.12</td>\n",
       "      <td>6.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14614.0</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.26</td>\n",
       "      <td>6.08</td>\n",
       "      <td>6.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14615.0</td>\n",
       "      <td>6.20</td>\n",
       "      <td>6.23</td>\n",
       "      <td>6.10</td>\n",
       "      <td>6.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14616.0</td>\n",
       "      <td>6.11</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.01</td>\n",
       "      <td>6.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14617.0</td>\n",
       "      <td>6.01</td>\n",
       "      <td>6.08</td>\n",
       "      <td>5.99</td>\n",
       "      <td>6.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3622</th>\n",
       "      <td>20076.0</td>\n",
       "      <td>8.52</td>\n",
       "      <td>8.58</td>\n",
       "      <td>8.40</td>\n",
       "      <td>8.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3623</th>\n",
       "      <td>20077.0</td>\n",
       "      <td>8.47</td>\n",
       "      <td>8.60</td>\n",
       "      <td>8.46</td>\n",
       "      <td>8.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3624</th>\n",
       "      <td>20080.0</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.73</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3625</th>\n",
       "      <td>20081.0</td>\n",
       "      <td>8.60</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.59</td>\n",
       "      <td>8.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3626</th>\n",
       "      <td>20082.0</td>\n",
       "      <td>8.78</td>\n",
       "      <td>9.02</td>\n",
       "      <td>8.77</td>\n",
       "      <td>8.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3627 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  open  high   low  close\n",
       "0     14613.0  6.21  6.27  6.12   6.12\n",
       "1     14614.0  6.15  6.26  6.08   6.21\n",
       "2     14615.0  6.20  6.23  6.10   6.11\n",
       "3     14616.0  6.11  6.15  6.01   6.02\n",
       "4     14617.0  6.01  6.08  5.99   6.04\n",
       "...       ...   ...   ...   ...    ...\n",
       "3622  20076.0  8.52  8.58  8.40   8.48\n",
       "3623  20077.0  8.47  8.60  8.46   8.48\n",
       "3624  20080.0  8.45  8.73  8.45   8.62\n",
       "3625  20081.0  8.60  8.78  8.59   8.78\n",
       "3626  20082.0  8.78  9.02  8.77   8.88\n",
       "\n",
       "[3627 rows x 5 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataf1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 拟合开始，数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c047a0f380>]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "#拟合开始\n",
    "#\n",
    "training_set = dataf1.iloc[0:2048, 4:5].to_numpy()#要提取一列数据，否则会出错\n",
    "test_set     = dataf1.iloc[3626 - 300:, 4:5].to_numpy()\n",
    "#print(training_set)\n",
    "plt.plot(training_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据归一化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将数据归一化，范围是0到1\n",
    "sc  = MinMaxScaler(feature_range=(0, 1))\n",
    "training_set_scaled = sc.fit_transform(training_set)\n",
    "testing_set_scaled  = sc.transform(test_set) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据分割"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取前 n_timestamp 天的数据为 X；n_timestamp+1天数据为 Y。\n",
    "def data_split(sequence, n_timestamp):\n",
    "    X = []\n",
    "    y = []\n",
    "    for i in range(len(sequence)):\n",
    "        end_ix = i + n_timestamp\n",
    "        \n",
    "        if end_ix > len(sequence)-1:\n",
    "            break\n",
    "            \n",
    "        seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]\n",
    "        X.append(seq_x)\n",
    "        y.append(seq_y)\n",
    "    return array(X), array(y)\n",
    "\n",
    "X_train, y_train = data_split(training_set_scaled, n_timestamp)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练数据重整"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train          = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n",
    "\n",
    "X_test, y_test   = data_split(testing_set_scaled, n_timestamp)\n",
    "X_test           = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 构建模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\86184\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:200: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)          │        <span style=\"color: #00af00; text-decoration-color: #00af00\">10,400</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">20,200</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">51</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m50\u001b[0m)          │        \u001b[38;5;34m10,400\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │        \u001b[38;5;34m20,200\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m51\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#五、构建模型\n",
    "# 建构 LSTM模型\n",
    "model_type=2\n",
    "if model_type == 1:\n",
    "    # 单层 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=50, activation='relu',\n",
    "                   input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(Dense(units=1))\n",
    "if model_type == 2:\n",
    "    # 多层 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=50, activation='relu', return_sequences=True,\n",
    "                   input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(LSTM(units=50, activation='relu'))\n",
    "    model.add(Dense(1))\n",
    "if model_type == 3:\n",
    "    # 双向 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(Bidirectional(LSTM(50, activation='relu'),\n",
    "                            input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(Dense(1))\n",
    "\n",
    "model.summary()  # 输出模型结构"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型编译"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=tf.keras.optimizers.Adam(0.001),\n",
    "              loss='mean_squared_error')  # 损失函数用均方误差\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 26ms/step - loss: 0.0778 - val_loss: 0.0793\n",
      "Epoch 2/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0119 - val_loss: 0.0013\n",
      "Epoch 3/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0018 - val_loss: 0.0011\n",
      "Epoch 4/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0011 - val_loss: 9.5937e-04\n",
      "Epoch 5/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 8.2600e-04 - val_loss: 0.0011\n",
      "Epoch 6/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 7.9397e-04 - val_loss: 0.0012\n",
      "Epoch 7/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 7.8494e-04 - val_loss: 0.0012\n",
      "Epoch 8/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 7.7394e-04 - val_loss: 0.0012\n",
      "Epoch 9/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 7.6317e-04 - val_loss: 0.0011\n",
      "Epoch 10/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 7.5480e-04 - val_loss: 0.0011\n",
      "Epoch 11/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 7.4796e-04 - val_loss: 0.0011\n",
      "Epoch 12/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 7.4292e-04 - val_loss: 0.0011\n",
      "Epoch 13/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 7.3828e-04 - val_loss: 0.0010\n",
      "Epoch 14/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 7.3461e-04 - val_loss: 0.0010\n",
      "Epoch 15/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 7.3079e-04 - val_loss: 0.0010\n",
      "Epoch 16/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 7.2706e-04 - val_loss: 0.0010\n",
      "Epoch 17/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 7.2322e-04 - val_loss: 0.0010\n",
      "Epoch 18/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 7.2123e-04 - val_loss: 0.0010\n",
      "Epoch 19/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 7.1781e-04 - val_loss: 0.0010\n",
      "Epoch 20/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 7.1473e-04 - val_loss: 0.0010\n",
      "Epoch 21/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 7.1190e-04 - val_loss: 0.0010\n",
      "Epoch 22/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 7.0657e-04 - val_loss: 0.0010\n",
      "Epoch 23/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 7.0254e-04 - val_loss: 0.0010\n",
      "Epoch 24/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 6.9833e-04 - val_loss: 0.0010\n",
      "Epoch 25/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 6.9370e-04 - val_loss: 0.0010\n",
      "Epoch 26/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 6.8689e-04 - val_loss: 0.0010\n",
      "Epoch 27/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 6.8020e-04 - val_loss: 0.0010\n",
      "Epoch 28/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 6.7385e-04 - val_loss: 0.0010\n",
      "Epoch 29/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 6.6671e-04 - val_loss: 9.9387e-04\n",
      "Epoch 30/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 6.5894e-04 - val_loss: 9.8427e-04\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)          │        <span style=\"color: #00af00; text-decoration-color: #00af00\">10,400</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">20,200</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">51</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m50\u001b[0m)          │        \u001b[38;5;34m10,400\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │        \u001b[38;5;34m20,200\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m51\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">91,955</span> (359.20 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m91,955\u001b[0m (359.20 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">61,304</span> (239.47 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m61,304\u001b[0m (239.47 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#七、训练模型\n",
    "history = model.fit(X_train, y_train,\n",
    "                    batch_size=64,\n",
    "                    epochs=n_epochs,\n",
    "                    validation_data=(X_test, y_test),\n",
    "                    validation_freq=1)  # 测试的epoch间隔数\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输出训练结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "#plt.title('Training and Validation Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 56ms/step\n"
     ]
    }
   ],
   "source": [
    "predicted_stock_price = model.predict(\n",
    "    X_test)                        # 测试集输入模型进行预测\n",
    "predicted_stock_price = sc.inverse_transform(\n",
    "    predicted_stock_price)  # 对预测数据还原---从（0，1）反归一化到原始范围\n",
    "real_stock_price = sc.inverse_transform(y_test)  # 对真实数据还原---从（0，1）反归一化到原始范围"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画出真实数据和预测数据的对比曲线\n",
    "plt.plot(real_stock_price, color='red', label='Stock Price')\n",
    "plt.plot(predicted_stock_price, color='blue', label='Predicted Stock Price')\n",
    "plt.title(stockname)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Stock Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型校核"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "均方误差: 0.03579\n",
      "均方根误差: 0.18918\n",
      "平均绝对误差: 0.15125\n",
      "R2: 0.89672\n"
     ]
    }
   ],
   "source": [
    "MSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)\n",
    "RMSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)**0.5\n",
    "MAE = metrics.mean_absolute_error(predicted_stock_price, real_stock_price)\n",
    "R2 = metrics.r2_score(predicted_stock_price, real_stock_price)\n",
    "\n",
    "print('均方误差: %.5f' % MSE)\n",
    "print('均方根误差: %.5f' % RMSE)\n",
    "print('平均绝对误差: %.5f' % MAE)\n",
    "print('R2: %.5f' % R2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.8"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
